Balanced Hashing  

作　　者：GONG Dahan CHU Chaoqun CHEN Kai GUO Yuchen DING Guiguang 

机构地区：[1]School of Software, Tsinghua University

出　　处：《Chinese Journal of Electronics》2019年第6期1204-1209,共6页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61571269,No.61971260)

摘　　要：Hashing for nearest neighbor search has attracted considerable interest recently given its efficiency in speed and storage.Many methods follow a projection-quantization framework which firstly projects original data into low-dimensional compact space and secondly quantifies each projected dimension to 1 bit by thresholding.The variance of projected dimensions,however,may vary a lot so that quantifying them equivalently degrades the searching performance.In this paper,we put forward a novel method,dubbed Balanced hashing(BH),which finds adjustment functions to reproject the data such that the variance of dimensions can be balanced by directly and explicitly maximizing the degree of balance of data,while preserving important properties.Experiments on benchmarks demonstrate that BH can outperform several state-of-the-art methods.Hashing for nearest neighbor search has attracted considerable interest recently given its efficiency in speed and storage. Many methods follow a projection-quantization framework which firstly projects original data into low-dimensional compact space and secondly quantifies each projected dimension to 1 bit by thresholding. The variance of projected dimensions,however, may vary a lot so that quantifying them equivalently degrades the searching performance. In this paper, we put forward a novel method, dubbed Balanced hashing(BH), which finds adjustment functions to reproject the data such that the variance of dimensions can be balanced by directly and explicitly maximizing the degree of balance of data, while preserving important properties. Experiments on benchmarks demonstrate that BH can outperform several state-of-the-art methods.

关 键 词：HASHING BALANCE OPTIMIZATION RETRIEVAL 

分 类 号：TN[电子电信]